Abstract Since Ramon y Cajal, neuroscientists have speculated that even the most complex brain functions might even- tually be understood at the level of neuronal cell types and their connections. More recently, while we have be- gun to understand the wiring principles of cortical microcircuit in rodents at the level of cell types, we are still in infancy in understanding the circuit organization of the primate neocortex at the level of cell types and their connections, slowing the progress toward a mechanistic understanding of complex cognitive capabilities char- acteristic of primates. For instance, the dorsolateral prefrontal cortex (DLPFC) of the primate brain is the most evolutionarily developed brain region that supports complex cognitive processes characteristic of primates, such as reasoning, planning, and abstract thinking. However, we know little about the constituent cell types comprising DLPFC circuit, how each cell type connects each other to form a functional circuit, and what circuit components specific to this circuit endow it with superb computational capabilities for complex cognitive pro- cesses. To fill in this knowledge gap, we scale up a cost-effective, interdisciplinary approach to macaque DLPFC, aiming at identifying all its consitiuent cell types and decipher their connectivity rules, with emphasis on highly diverse GABAergic interneurons. We propose to use multi-cell patch recordings, single-cell RNA sequencing (scRNA-seq), novel and rapid viral GABAergic labeling, and machine learning to achieve two main goals: 1) dissect macaque DLPFC microcircuit by generating a morphological taxonomy of cell types in DLPFC and mapping their connections; and 2) derive transcriptomic signatures of morphologically defined DLPFC neurons using Patch-seq method, a novel scRNA-seq protocol. We have demonstrated the feasibility and suc- cess of this approach in mouse neocortex, and our preliminary data indicate no technical issue in applying this approach to primates. Using multi-cell patch recordings, we will characterize electrophysiology and morphology of thousands of neurons and map connections between tens of thousands of cell pairs from DLPFC. Using Patch-seq method, we will combine patch recording with a novel/sensitive scRNA-seq method (Smart-seq2) to simultaneously obtain electrophysiology, morphology and transcriptome from single neurons, which can further substantiate cell type classification and identify novel molecular markers for each cell type. We will prioritize our effort on superficial layers of DLPFC, but eventually scale up our efforts to all layers if time permits. At the end, the project will uncover a high-resolution microcircuit blueprint of macaque DLPFC with each circuit com- ponent identified by specific genetic markers. Such a comprehensive dataset will provide the essential ground- work to design molecular tools for further functional dissection of the complex cognitive processes subserved by PFC. From a clinical perspective, having reference transcriptomes and connectivity patterns for different cell types in primate DLPFC will facilitate our understanding of the relationship between disease-associated genes, cell types, and circuit deficits in neuropsychiatric diseases, schizophrenia in particular.